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Journal articles on the topic 'Handwritten Sindhi character recognition'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Abdul Naveed, Jakhro, Mudasar Ahmed Soomro, Leezna Saleem, and Muhammad Khalid Shaikh. "OHSCR: Benchmarks Dataset for Offline Handwritten Sindhi Character Recognition." Sir Syed University Research Journal of Engineering & Technology 14, no. 1 (2024): 55–61. http://dx.doi.org/10.33317/ssurj.618.

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This research work presents a unique dataset for offline handwritten Sindhi character recognition. It has 7800 character images in total, divided into multiple categories by 150 writers of various ages, genders, and professional backgrounds. Each writer writes the 52 Sindhi characters in the designed form. With a high-quality scanner, all of the written samples were scanned. After that, all the handwritten Sindhi characters were cropped from the collected designed form, and the cropped images were saved in ‘.png’ format. For the benefit of the Sindhi research community, this work suggests an i

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Asghar, Ali Chandio, Leghari Mehwish, Orangzeb Panhwar Ali, Zaman Nizamani Shah, and Leghari Mehjabeen. "Deep learning-based isolated handwritten Sindhi character recognition." Indian Journal of Science and Technology 13, no. 25 (2020): 2565–74. https://doi.org/10.17485/IJST/v13i25.914.

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Abstract <strong>Motivation :</strong> The problem of handwritten text recognition is vastly studied since last few decades. Many innovative ideas have been developed, where state-of-the-art accuracy is achieved for the English, Chinese or Indian scripts.The recent developments for the cursive scripts such as Arabic and Urdu handwritten text recognition have achieved remarkable accuracy. However, for the Sindhi script, existing systems have not shown significant results and the problem is still an open challenge. Several challenges such as variations in writing styles, joined text, ligatu

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Shafique, A. Awan, Nawaz Hakro Dil, Lashari Intzar, H. Jalbani Akhtar, and Hameed Maryam. "A Complete Off-line Sindhi Handwritten Text Recognition: A Survey." International Journal of Management Sciences and Business Research 6, no. 4 (2017): 131–38. https://doi.org/10.5281/zenodo.3469359.

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Artificial Intelligence is finding ways to make machines more intelligent and work like human being. Image processing, Natural language processing and Optical Character Recognition (OCR) are the active fields of computer vision, where the computers are made more versatile to understand, read and write natural human languages spoken around the word. Optical Characters Recognition (OCR) and Intelligent Characters Recognition (ICR) differ in recognizing printed and handwritten characters respectively. Intelligent Characters Recognition (ICR) is an active field in which handwritten characters are

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Awan, Shafique Ahmed, Zahid Hussain Abro, Akhtar Hussain Jalbani, Dil Nawaz Hakro, and Maryam Hameed. "Handwritten Sindhi Character Recognition Using Neural Networks." Mehran University Research Journal of Engineering and Technology 37, no. 1 (2018): 191–96. http://dx.doi.org/10.22581/muet1982.1801.17.

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Siddiqui, Sayma Shafeeque A. W., Rajashri G. Kanke, Ramnath M. Gaikwad, and Manasi R. Baheti. "Review on Isolated Urdu Character Recognition: Offline Handwritten Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 384–88. http://dx.doi.org/10.22214/ijraset.2023.55164.

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Abstract: This paper summarizes a system for recognizing isolated Urdu characters using advanced machine learning algorithms. The system analyzes visual features of Urdu characters, like strokes and curves, to train models such as CNN, SVM, ANN, and MLP. With a large dataset, the system can accurately predict unseen characters. It can be integrated into various applications for real-time character recognition tasks like OCR (Optical Character Recognition) and handwriting recognition. This literature survey explores research papers focused on character recognition in languages like Urdu, Arabic

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6

Chandio, Asghar Ali. "Deep learning-based isolated handwritten Sindhi character recognition." Indian Journal of Science and Technology 13, no. 25 (2020): 2565–74. http://dx.doi.org/10.17485/ijst/v13i25.914.

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7

Shafique, A. Awan, Nawaz Hakro Dil, Ali Lashari Intzar, Hussain Zahid, H. Jalbani Akhtar, and Hameed Maryam. "A Comprehensive Database for Offline Sindhi Handwritten Text Recognition." International Journal of Case Studies 6, no. 3 (2017): 72–82. https://doi.org/10.5281/zenodo.3534899.

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A well designed image database is very necessary for the recognition of any language and many of language of the world have their own database for the text recognition. In this paper we are presenting the comprehensive database for Sindhi Language which is highly demanding language of the Middle East countries. The database will consist of the many words, which are written by the many writers. This is novel approach for the creating and testing of Sindhi Text. This database contains the isolated characters as well as the text of Sindhi language. The database is consisting of the images of the

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Kumari, Arsha, Din Muhammad Sangrasi, Sania Bhatti, Bhawani Shankar Chowdhry, and Sapna Kumari. "Off-line Sindhi Handwritten Character Identification." International Journal of Information Technology and Computer Science 11, no. 6 (2019): 9–17. http://dx.doi.org/10.5815/ijitcs.2019.06.02.

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9

Firdous, Saniya. "Handwritten Character Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1409–28. http://dx.doi.org/10.22214/ijraset.2022.42114.

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Wadaskar, Ghanshyam, Vipin Bopanwar, Prayojita Urade, Shravani Upganlawar, and Prof Rakhi Shende. "Handwritten Character Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 508–11. http://dx.doi.org/10.22214/ijraset.2023.57366.

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Abstract: Handwritten character recognition is a fascinating topic in the field of artificial intelligence. It involves developing algorithms and models that can analyze and interpret handwritten characters, such as letters, numbers, or symbols. The goal is to accurately convert handwritten text into digital form, making it easier to process and understand. It's a complex task, but with advancements in machine learning and deep learning techniques, significant progress has been made in this area.Handwritten character recognition is all about teaching computers to understand and interpret handw

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Tirapathi Reddy B. "Handwritten Character Recognition System." Journal of Electrical Systems 20, no. 3 (2024): 1465–75. http://dx.doi.org/10.52783/jes.3553.

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Digitizing handwritten documents and enabling efficient information processing and retrieval require systems that can recognize handwritten characters. This research offers a unique approach for handwritten character detection using state-of-the-art machine learning algorithms. The proposed technique automatically extracts discriminative features from photos of handwritten characters using convolutional neural networks (CNNs). These attributes are then used by a classifier to determine which characters are related. The dataset used for training and assessment is made up of a large collection o

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Abhale, Poonam Bhanudas. "Handwritten English Alphabet Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 2134–39. http://dx.doi.org/10.22214/ijraset.2021.39703.

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Abstract: Character recognition is a process by which a computer recognizes letters, figures, or symbols and turns them into a digital form that a computer can use. In moment’s terrain character recognition has gained a lot of attention in the field of pattern recognition. Handwritten character recognition is useful in cheque processing in banks, form recycling systems, and numerous further. Character recognition is one of the well- liked and grueling areas of exploration. In the unborn character recognition produce a paperless terrain. In this paper, we describe the detailed study of the bein

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Tiptur Parashivamurthy, Supreetha Patel, and Dr Sannangi Viswaradhya Rajashekararadhya. "An Efficient Kannada Handwritten Character Recognition Framework with Serial Dilated Cascade Network for Kannada Scripts." Advances in Artificial Intelligence and Machine Learning 04, no. 03 (2024): 2499–516. http://dx.doi.org/10.54364/aaiml.2024.43146.

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The most significant problem present in the digitized world is handwritten character recognition and identification because it is helpful in various applications. The manual work needed for changing the handwritten character document into machine-readable texts is highly reduced by using the automatic identification approaches. Due to the factors of high variance in the writing styles beyond the globe, handwritten text size and low quality of handwritten text rather than printed text make handwritten character recognition to be very complex. The Kannada language has originated over the past 10

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Chandio, Asghar Ali, Akhtar Hussian Jalbani, Mehwish Laghari, and Shafique Ahmed Awan. "Multi-Digit Handwritten Sindhi Numerals Recognition using SOM Neural Network." Mehran University Research Journal of Engineering and Technology 36, no. 4 (2017): 901–8. http://dx.doi.org/10.22581/muet1982.1704.14.

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Naik, Dr Vishal, and Heli Mehta. "Comparison of Various Algorithms for Handwritten Character Recognition of Indian Languages." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 696–703. http://dx.doi.org/10.22214/ijraset.2023.56079.

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Abstract: In this paper, we present a comparison of various pre-processor, feature extraction methods and algorithms for handwritten character recognition of various Indian languages. Comparison of classifier, feature set and accuracy of offline handwritten character recognition of Gujarati, Devanagari, Gurmukhi, Kannada, Malayalam, Bangla and Hindi Indian languages. Comparison of classifier, feature set and accuracy of online handwritten character recognition of Assamese, Tamil, Devanagari, Malayalam, Gurmukhi, and Bangla Indian languages. Indian language wise best performance of each languag

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Ning, Zihao. "Research on Handwritten Chinese Character Recognition Based on BP Neural Network." Modern Electronic Technology 6, no. 1 (2022): 12. http://dx.doi.org/10.26549/met.v6i1.11359.

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The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before. Handwritten Chinese character recognition, as a hot research object in image pattern recognition, has many applications in people’s daily life, and more and more scholars are beginning to study off-line handwritten Chinese character recognition. This paper mainly studies the recognition of handwritten Chinese characters by BP (Back Propagation) neural network. Establish a handwritten Chinese character recognition model based on BP neural network

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Somashekar, Thatikonda. "A Survey on Handwritten Character Recognition using Machine Learning Technique." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1019–24. http://dx.doi.org/10.51201/jusst/21/05304.

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Due to its broad range of applications, handwritten character recognition is widespread. Processing application forms, digitizing ancient articles, processing postal addresses, processing bank checks, and many other handwritten character processing fields are increasing in popularity. Since the last three decades, handwritten characters have drawn the attention of researchers. For successful recognition, several methods have been suggested. This paper presents a comprehensive overview of handwritten character recognition using a neural network as a machine learning tool.

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Wakahara, Toru. "Toward robust handwritten character recognition." Pattern Recognition Letters 14, no. 4 (1993): 345–54. http://dx.doi.org/10.1016/0167-8655(93)90100-r.

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Soselia, Davit, Magda Tsintsadze, Levan Shugliashvili, Irakli Koberidze, Shota Amashukeli, and Sandro Jijavadze. "On Georgian Handwritten Character Recognition." IFAC-PapersOnLine 51, no. 30 (2018): 161–65. http://dx.doi.org/10.1016/j.ifacol.2018.11.279.

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Santosh, Acharya, Dhungel Shashank, and Kr. Jha Ashish. "Nepali Handwritten Character Recognition System." Advancement in Image Processing and Pattern Recognition 5, no. 3 (2022): 1–6. https://doi.org/10.5281/zenodo.7472398.

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Even if the technological and digital world is expanding more quickly, there are still many things that are lacking. What a wonderful thing it would be to be able to trust machines to scan any handwritten characters into digital representation. The method for doing this is called optical character recognition (OCR), but there is still much room for improvement. Although there has been work done on it, the technique developed for one language cannot be applied to another due to language variations. Nepali is not a language that is frequently used online. Perhaps this is why there are fewer OCR

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Shrivastav, Jitendra, Ravindra Kumar Gupta, and Shailendra Singh. "A Modified Back propagation Algorithm for Optical Character Recognition." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 06 (2013): 180–84. https://doi.org/10.5281/zenodo.14605792.

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Character Recognition (CR) has been an active area of research and due to its diverse applicable environment; it continues to be a challenging research topic. There is a clear need for optical character recognition in order to provide a fast and accurate method to search both existing images as well as large archives of existing paper documents. However, existing optical character recognition programs suffer from a flawed tradeoff between speed and accuracy, making it less attractive for large quantities of documents. In this thesis, we present a new neural network based method for optical cha

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Haithem Abd Al-RaheemTaha. "ON-LINE HANDWRITTEN ARABIC CHARACTER RECOGNITION BASED ON GENETIC ALGORITHM." Diyala Journal of Engineering Sciences 5, no. 1 (2012): 79–87. http://dx.doi.org/10.24237/djes.2012.05107.

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On-line Arabic handwritten character recognition is one of the most challenging problems in pattern recognition field. By now, printed Arabic character recognition and on-line Arabic handwritten recognition has been gradually practical, while offline Arabic handwritten character recognition is still considered as "The hardest problem to conquer" in this field due to its own complexity. Recently, it becomes a hot topic with the release of database, which is the first text-level database and is concerned about the area of realistic Arabic handwritten character recognition. At the realistic

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JYOTI, A.PATIL, and SANJAY R. PATIL DR. "OPTICAL HANDWRITTEN DEVNAGARI CHARACTER RECOGNITION USING ARTIFICIAL NEURAL NETWORK APPROACH." IJIERT - International Journal of Innovations in Engineering Research and Technology 5, no. 3 (2018): 67–71. https://doi.org/10.5281/zenodo.1454101.

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<strong>Character recognitions play a wide role in the fast moving world with the growing technology,by providing more scope to perform research in OCR techniques. In the field of pattern recognition Devnagari handwritten character recognition is one of the challenging research area. Character recognition is defined as electronic translation of scanned images of handwritten or printed text into a machine encoded text. In this paper proposed an off line handwritten Devnagari character recognition technique with the use of feed forward neural network. For training the neural network a handwritte

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N D, Sukesh, and Steephan Amalraj J. "Handwritten Character Recognition Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25945.

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Handwritten digit or character recognition in transforming the printed or handwritten text from an image. Optical character recognition plays an important role in documentation scanning ,text extractions from the image. Optical character recognition is used in different fields like postal services ,Ecommerce , Shipping ,Banking sector for character extraction from the images . However the existing character recognition system faces many challenges in extracting text from noisy and distortion images or complex layout and Extraction mostly limited to numbers and English alphabets . The introduct

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Alharbi, Abir. "A Genetic-LVQ neural networks approach for handwritten Arabic character recognition." Artificial Intelligence Research 7, no. 2 (2018): 43. http://dx.doi.org/10.5430/air.v7n2p43.

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Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dat

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Riasat, Azim, Fazlul Karim M., and Rahman Wahidur. "Bangla Hand Written Character Recognition Using Support Vector Machine." International Journal of Engineering Works (ISSN: 2409-2770) 3, no. 6 (2016): 36–46. https://doi.org/10.5281/zenodo.60329.

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Recognizing handwritten character using computer is still consider a strong area of research. A fundamental problem in the field of Bangla character recognition is the lack of availability of Bangla handwritten character data set. In this thesis our main objective is to generate a larger dataset of Bangla character and as well as improving the recognition rate using Support Vector Machine. Support Vector Machines (SVM) is used for classification in pattern recognition widely. In our proposed method we applied support vector machine for increasing the recognition rate. A scanner is used to capt

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Jehangir, Sardar, Sohail Khan, Sulaiman Khan, Shah Nazir, and Anwar Hussain. "Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis." January 2021 40, no. 1 (2021): 152–59. http://dx.doi.org/10.22581/muet1982.2101.14.

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This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is

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Sameer, Bawaneh. "Handwritten Recognition (numbers)." European Journal of Information technology and Project Management 1, no. 2 (2019): 7–12. https://doi.org/10.5281/zenodo.3229015.

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<strong><em>due to the magnitude of the neural network science and MATLAB in terms of tools and algorithms, we will present a simple algorithm and explain it in general in this project, on the other hand due to we do not have enough knowledge in this field. In this project we will provide an overview of the role of handwriting recognition in neural network by using Optical Character Recognition algorithm.</em></strong>

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Ali, Wazir, Jay Kumar, Zenglin Xu, Rajesh Kumar, and Yazhou Ren. "Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition." Applied Sciences 11, no. 19 (2021): 9038. http://dx.doi.org/10.3390/app11199038.

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Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yield excellent performance in NER by taking advantage of character-level and word-level representation learning. In this paper, we propose a deep context-aware bidirectional long short-term memory (CaBiLSTM) model for the Sindhi NER task. The model relies upon contextual representation learning (CRL), bidirectional encoder, self-attention, and sequential cond

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BATUWITA, RUKSHAN, VASILE PALADE, and DHARMAPRIYA C. BANDARA. "A CUSTOMIZABLE FUZZY SYSTEM FOR OFFLINE HANDWRITTEN CHARACTER RECOGNITION." International Journal on Artificial Intelligence Tools 20, no. 03 (2011): 425–55. http://dx.doi.org/10.1142/s021821301100022x.

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Automated offline handwritten character recognition involves the development of computational methods that can generate descriptions of the handwritten objects from scanned digital images. This is a challenging computational task, due to the vast impreciseness associated with the handwritten patterns of different individuals. Therefore, to be successful, any solution should employ techniques that can effectively handle this imprecise knowledge. Fuzzy Logic, with its ability to deal with the impreciseness arisen due to lack of knowledge, could be successfully used to develop automated systems f

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Memon, Nisar Ahmed, Fatima Abbasi, and Shehnila Zardari. "Glyph Identification and Character Recognition for Sindhi OCR." Mehran University Research Journal of Engineering and Technology 36, no. 4 (2017): 933–40. http://dx.doi.org/10.22581/muet1982.1704.18.

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Manoj, Sonkusare, and Sahu Narendra. "A SURVEY ON HANDWRITTEN CHARACTER RECOGNITION (HCR) TECHNIQUES FOR ENGLISH ALPHABETS." Advances in Vision Computing: An International Journal (AVC) 3, no. 1 (2016): 01–12. https://doi.org/10.5281/zenodo.3461522.

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Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research

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Manoj, Sonkusare, and Sahu Narendra. "A SURVEY ON HANDWRITTEN CHARACTER RECOGNITION (HCR) TECHNIQUES FOR ENGLISH ALPHABETS." Advances in Vision Computing: An International Journal (AVC) 3, no. 1 (2016): 01–12. https://doi.org/10.5281/zenodo.3626432.

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Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research

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Liu, Hanqi. "Handwritten English character recognition using convolutional neural network." Applied and Computational Engineering 4, no. 1 (2023): 199–204. http://dx.doi.org/10.54254/2755-2721/4/20230450.

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Converting paper material into electronic material is still a necessary work nowadays, however, recognition of handwritten characters still has limitations in their recognition rate, owing to the presence of various shapes, scales, and formats in different peoples handwritten characters. Machine learning has significant value in reducing human power. A Convolutional Neural Network model that is revised from LeNet-5, is used for handwritten letter recognition. This study uses the EMINST dataset to train the model, and the final recognition rate is about 93.44%.

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Teja, K. Sai. "Hindi-Handwritten-Character- Recognition using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 369–73. http://dx.doi.org/10.22214/ijraset.2023.54606.

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Abstract: Hindi-Handwritten-Character- Recognition is animportant problem in the field of machine learning andcomputer vision. With the increasing digitization of India, there is a growing need to develop accurate and efficient algorithms for recognizing handwritten Hindi characters, which can be used in a variety of applications such as document analysis, postal automation, and data entry. In recent years, deep learning has emerged as a powerful tool for solving complex recognition problems. In this work, we propose a deep learning-based approach to the Hindi-Handwritten Character-Recognition

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Ishan, Gulati*1 Gautam Vig2 &. Vijay Khare3. "REAL TIME HANDWRITTEN CHARACTER RECOGNITION USING ANN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 357–62. https://doi.org/10.5281/zenodo.1218609.

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<em>-</em>Real time  Handwritten Character Recognition by using Template Matching is a system which is useful to recognize the character or alphabets in the given text by comparing two images of the alphabet. The objectives of this system prototype are to develop a program for the Optical Character Recognition (OCR) system by using the Template Matching algorithm . Handwritten character recognition is a challenging task in the field of research on image processing, artificial intelligence as well as machine vision since the handwriting varies from person to person. Moreover, the handwriti

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Mujadded, Al Rabbani Alif. "State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture." State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture 9, no. 1 (2024): 11. https://doi.org/10.5281/zenodo.10538255.

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Bangla Handwritten Character Recognition (HCR) remains a persistent challenge within the domain of Optical Character Recognition (OCR) systems. Despite extensive research efforts spanning several decades, achieving satisfactory success in this field has proven to be complicated. Bangla, being one of the most widely spoken languages worldwide, consists of 50 primary characters, including 11 vowels and 39 consonants. Unlike Latin languages, Bangla characters exhibit complex patterns, diverse sizes, significant variations, intricate letter shapes, and intricate edges. These characteristics furthe

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N S, Aswin. "Malayalam Handwritten Words Recognition: A Review." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30057.

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This review examines character segmentation and offers an elegant method for identifying and transforming handwritten Malayalam words from picture documents into text. Character touchings, different writing styles, and noisy, damaged scanned photos make it difficult to recognise handwritten text. Taking use of today's world of rich data and algorithmic developments, the system uses deep convolutional neural networks (CNNs) to address these challenges. The three steps of Malayalam handwritten word recognition are segmentation, recognition, and pre-processing. Making Malayalam character datasets

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Amulya, K., Lakshmi Reddy, M. Chandara Kumar, and Rachana D. "A Survey on Digitization of Handwritten Notes in Kannada." International Journal of Innovative Technology and Exploring Engineering 12, no. 1 (2022): 6–11. http://dx.doi.org/10.35940/ijitee.a9350.1212122.

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Recognition of handwritten text is still an unresolved research problem in the field of optical character recognition. This article suggests an efficient method for creating handwritten text recognition systems. This is a challenging subject that has received a lot of attention recently. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analyzable data. Researchers have been using artificial intelligence and machine learning methods to automatically evaluate printed and handwritten documents during the

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K, Amulya, Reddy Lakshmi, Chandara Kumar M, and D. Rachana. "A Survey on Digitization of Handwritten Notes in Kannada." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 12, no. 1 (2022): 6–11. https://doi.org/10.35940/ijitee.A9350.1212122.

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<strong>Abstract: </strong>Recognition of handwritten text is still an unresolved research problem in the field of optical character recognition. This article suggests an efficient method for creating handwritten text recognition systems. This is a challenging subject that has received a lot of attention recently. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analyzable data. Researchers have been using artificial intelligence and machine learning methods to automatically evaluate printed and handwr

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Bhagat, Ms Shubhangee S. "Handwritten Character Detection Using Optical Character Recognition Method." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 4724–26. http://dx.doi.org/10.22214/ijraset.2018.4775.

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Shaina*, Harpreet Kaur Bajaj. "ISOLATED CHARACTER RECOGNITION USING HIERARCHICAL APPROACH WITH SVM CLASSIFIER." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 9 (2016): 570–75. https://doi.org/10.5281/zenodo.154564.

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This paper proposes a method for Urdu language text. Character recognition is obtained by OCR. This paper represents the effectiveness of characters with SVM Classifier using Hierarchical approach. SVM is a useful technique for data classification. The objective of SVM is to generate a model which predicts the target value. The work is done on Sindhi Character Set. The experiment shows that character recognition with SVM Classifier achieves a recognition rate of 93.0481%.

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Kumar, K. Sathish, M. Shashivardhan Reddy, D. Hemanth Kumar, D. Shiva Kumar, N. Shiva, and Dr D. Thiyagarajan. "Hindi-Handwritten-Character-Recognition using Deep learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5252–55. http://dx.doi.org/10.22214/ijraset.2024.62768.

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Abstract: Recognizing handwritten Hindi characters poses a significant challenge in the realms of machine learning and computer vision, particularly in the context of India's accelerating digitization. To address this, accurate and efficient algorithms are imperative for applications ranging from document analysis to postal automation and data entry. Leveraging the advancements in deep learning, we propose a novel approach to Hindi Handwritten Character Recognition. Our method employs a combination of Convolutional Neural Networks (CNNs) to extract image features and Recurrent Neural Networks

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Neha*1, &. Deepti Ahlawat2. "HANDWRITTEN ALPHANUMERIC CHARACTER RECOGNITION AND COMPARISON OF CLASSIFICATION TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 1 (2018): 419–28. https://doi.org/10.5281/zenodo.1147604.

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Several techniques have been proposed by many researchers for handwritten as well as printed character and numerals recognition. Recognition is the process of conversion of handwritten text into machine readable form. To achieve the best accuracy of any recognition system the selection of feature extraction and classification technique is important. The data about the character is collected by the features and accordingly classifiers classify the character uniquely. For handwritten characters there are drawbacks like it differs from one writer to another, even when same person writes same char

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Mehta, Nikita, and Jyotika Doshi. "A Review of Handwritten Character Recognition." International Journal of Computer Applications 165, no. 4 (2017): 37–40. http://dx.doi.org/10.5120/ijca2017913855.

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Sahu, Manish Kumar, and Dr Naveen Kumar Dewangan. "A Survey on Handwritten Character Recognition." IARJSET 4, no. 1 (2017): 89–91. http://dx.doi.org/10.17148/iarjset.2017.4120.

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Sahu, Manish Kumar, and Naveen Kumar Dewangan. "Handwritten Character Recognition using Neural Network." IJARCCE 6, no. 6 (2017): 11–14. http://dx.doi.org/10.17148/ijarcce.2017.6603.

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Sinha, Gita, Dr Shailja Sharma, and Rakesh Kumar Roshan. "CLASSIFICATION TECHNIQUES FOR HANDWRITTEN CHARACTER RECOGNITION." International Journal of Engineering Applied Sciences and Technology 5, no. 3 (2020): 151–57. http://dx.doi.org/10.33564/ijeast.2020.v05i03.023.

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Li, Ling Hua, Shou Fang Mi, and Heng Bo Zhang. "Template-Based Handwritten Numeric Character Recognition." Advanced Materials Research 586 (November 2012): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.586.384.

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This paper describes a stroke-based handwriting analysis method in classifying handwritten Numeric characters by using a template-based approach. Writing strokes are variable from time to time, even when the writing character is same and comes from the same user. Writing strokes include the properties such as the number of the strokes, the shapes and sizes of them and the writing order and the writing speed. We describe here a template-based system using the properties of writing strokes for the recognition of online handwritten numeric characters. Experimental results show that within the 150

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Wang, Xian, Venu Govindaraju, and Sargur Srihari. "Holistic recognition of handwritten character pairs." Pattern Recognition 33, no. 12 (2000): 1967–73. http://dx.doi.org/10.1016/s0031-3203(99)00204-6.

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